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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteReflection AI announced Beam on October 5, 2026, as its first open-weight model for coding, reasoning, and agentic tasks. The company describes it as a 501-billion-parameter mixture-of-experts model with 23 billion active parameters. At announcement, Beam was still undergoing final evaluations; Reflection said it planned to release the weights and supporting materials later in October under Apache 2.0.
What is Reflection AI’s Beam model?
Beam is a sparse mixture-of-experts (MoE) language model. Its 501 billion total parameters describe the full model, while 23 billion are active for a given inference step, according to Reflection AI’s October 5, 2026 announcement. The distinction matters: active parameters are not the same as the model’s total storage or deployment requirements, and the announcement does not provide minimum hardware specifications.
Reflection says it designed Beam for coding, reasoning, and agentic workloads—tasks in which a model may use tools or operate through multi-step workflows. The company characterizes inference efficiency as a central goal. That positioning is a design claim, not a guarantee of lower serving cost in every setup.
When will Beam be released, and what does “open-weight” mean?
At launch, Reflection said Beam was still in final red-teaming and evaluations. It offered a waitlist for early access and targeted a later-October 2026 release of the weights, technical report, model card, and developer artifacts. The planned release was to include documentation and tools for running, evaluating, and fine-tuning the model. These were announcement-time plans, not confirmation that the materials have since shipped.
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Reflection announced Apache 2.0 as the planned license for the weights. Check the actual release files and license text before relying on that plan for commercial or other use. “Open-weight” also does not mean every part of model development is open: the reviewed company materials do not establish that all training data, processes, or components will be published. Reflection describes its broader idea of “open intelligence” as combining model weights, published research, and open-source software on its About page.
What training approach and scale did Reflection report?
The figures below are Reflection’s own descriptions in its October 2026 announcement; the sources reviewed do not independently audit them.
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- Pretraining: Reflection says it used 23.8 trillion tokens from curated web and licensed datasets.
- Reinforcement learning: The company reports more than 100 million rollouts, generated over four weeks using 10.5K NVIDIA GB300 GPUs.
- Data focus: Reflection says its pipeline emphasized source code, technical explanations, mathematics, and scientific knowledge, using quality classifiers and fine-grained quality tiers.
- Curation claims: It says about 95% of raw Internet tokens were removed through parsing, deduplication, and curation. It also says its process retained roughly 1.8 trillion high-quality tokens conventional techniques would have missed, including 87% of its curated web-code tokens.
These training figures describe Reflection’s reported process; by themselves, they do not establish model quality or make Beam’s results directly comparable with models trained under different conditions.
What do Beam’s announced benchmark results show?
Reflection’s benchmark table includes coding, agentic, reasoning, and STEM tasks. In two displayed agentic coding and terminal evaluations, the company reports a score of 44.4 on DeepSWE v1.1 and 77.2 on SWE Bench Pro v2-Hard. The scores belong to those named benchmark versions and should not be read as a single overall capability rating. Reflection’s table uses “NR” where comparison scores have not been reported.
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The results are vendor-presented evaluations, not independent validation. Reflection says Beam is competitive with larger open models such as GLM 5.2 and approaches Qwen 3.8-Max on coding and agentic tasks, while Kimi K3 remains ahead on raw capability; it presents Beam’s advantage as inference efficiency. The Information separately reported that Reflection said Beam outperformed Inkling and Nemotron 3 Ultra on certain coding and reasoning tests, but lagged leading Chinese models. These are company-attributed comparisons tied to selected tasks, not universal rankings. See the full benchmark table and methodology claims in Reflection’s announcement.
For a useful comparison with another model, look at the exact task and benchmark version, the serving setup and inference cost, total versus active parameters, the actual license and available artifacts, deployment requirements, and whether results have been independently reproduced.
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How can developers access or deploy Beam?
At announcement, Reflection described plans to distribute Beam through partners and integrate it with open-source libraries and harnesses. TechCrunch reported planned distribution through hyperscalers and neoclouds, but the launch coverage did not identify a confirmed Beam hosting provider. Reflection’s company overview discusses enterprise, government, on-premises, and sovereign AI infrastructure ambitions; that positioning does not establish Beam’s availability through those channels.
The announcement provides no end-user hardware requirements. Reflection’s use of GB300 GPUs for training is not a recommended inference configuration, so it is not enough to choose a GPU, server, or hosting service. Wait for the published model documentation and confirmed distribution details before planning a deployment.
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